Automatic Speech Recognition
Transformers
qwen3-asr
latent-reasoning
test-time-compute
parameter-efficient
Instructions to use voidful/latentASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/latentASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/latentASR")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("voidful/latentASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 623 Bytes
262fa3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # Hugging Face Release
The public model repo is intended to be:
```text
voidful/latentASR
```
The repository hosts:
- LatentASR code
- adapter checkpoint
- documentation
- model card
- reproducibility outputs
## Upload
From this project root:
```bash
python hf_upload/upload_to_hf.py --repo-id voidful/latentASR
```
The script creates the model repo if needed and uploads the current folder.
## Download Checkpoint Programmatically
```python
from huggingface_hub import hf_hub_download
ckpt = hf_hub_download(
repo_id="voidful/latentASR",
filename="checkpoints/latentASR_adapter.pth",
)
print(ckpt)
```
|